resolve conflicts

.gitignore

@@ -0,0 +1,5 @@
+venv
+build
+dist
+speechlib.egg-info
+.env

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Navod Peiris
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

examples/.gitignore

@@ -0,0 +1,7 @@
+example1.wav
+temp
+segments
+pretrained_models
+audio_cache
+__pycache__
+logs

examples/README.md

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+##### Run transcribe.py for trancribing an audio file  
+
+##### Run preprocess.py for preprocessing an audio file  

examples/preprocess.py

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+from speechlib import PreProcessor
+
+file = "obama1.mp3"
+#initialize
+prep = PreProcessor()
+# convert mp3 to wav
+wav_file = prep.convert_to_wav(file)   
+
+# convert wav file from stereo to mono
+prep.convert_to_mono(wav_file)
+
+# re-encode wav file to have 16-bit PCM encoding
+prep.re_encode(wav_file)

examples/transcribe.py

@@ -0,0 +1,18 @@
+from speechlib import Transcriptor
+
+file = "obama1.wav"  # your audio file
+voices_folder = "voices" # voices folder containing voice samples for recognition
+language = "en"          # language code
+log_folder = "logs"      # log folder for storing transcripts
+modelSize = "tiny"     # size of model to be used [tiny, small, medium, large-v1, large-v2, large-v3]
+quantization = False   # setting this 'True' may speed up the process but lower the accuracy
+ACCESS_TOKEN = "your huggingface access token" # get permission to access pyannote/speaker-diarization@2.1 on huggingface
+
+# quantization only works on faster-whisper
+transcriptor = Transcriptor(file, log_folder, language, modelSize, ACCESS_TOKEN, voices_folder, quantization)
+
+# use normal whisper
+res = transcriptor.whisper()
+
+# use faster-whisper (simply faster)
+res = transcriptor.faster_whisper()

library.md

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+### Requirements
+
+* Python 3.8 or greater
+
+### GPU execution
+
+GPU execution needs CUDA 11.  
+
+GPU execution requires the following NVIDIA libraries to be installed:
+
+* [cuBLAS for CUDA 11](https://developer.nvidia.com/cublas)
+* [cuDNN 8 for CUDA 11](https://developer.nvidia.com/cudnn)
+
+There are multiple ways to install these libraries. The recommended way is described in the official NVIDIA documentation, but we also suggest other installation methods below.
+
+### Google Colab:
+
+on google colab run this to install CUDA dependencies:
+```
+!apt install libcublas11
+```
+
+You can see this example [notebook](https://colab.research.google.com/drive/1lpoWrHl5443LSnTG3vJQfTcg9oFiCQSz?usp=sharing)
+
+### installation:
+```
+pip install speechlib
+```
+
+This library does speaker diarization, speaker recognition, and transcription on a single wav file to provide a transcript with actual speaker names. This library will also return an array containing result information. ⚙ 
+
+This library contains following audio preprocessing functions:
+
+1. convert mp3 to wav
+
+2. convert stereo wav file to mono
+
+3. re-encode the wav file to have 16-bit PCM encoding
+
+Transcriptor method takes 6 arguments. 
+
+1. file to transcribe
+
+2. log_folder to store transcription
+
+3. language used for transcribing (language code is used)
+
+4. model size ("tiny", "small", "medium", "large", "large-v1", "large-v2", "large-v3")
+
+5. voices_folder (contains speaker voice samples for speaker recognition)
+
+6. quantization: this determine whether to use int8 quantization or not. Quantization may speed up the process but lower the accuracy.
+
+voices_folder should contain subfolders named with speaker names. Each subfolder belongs to a speaker and it can contain many voice samples. This will be used for speaker recognition to identify the speaker.
+
+if voices_folder is not provided then speaker tags will be arbitrary.
+
+log_folder is to store the final transcript as a text file.
+
+transcript will also indicate the timeframe in seconds where each speaker speaks.
+
+### Transcription example:
+
+```
+from speechlib import Transcriptor
+
+file = "obama_zach.wav"
+voices_folder = "voices"
+language = "en"
+log_folder = "logs"
+modelSize = "medium"
+quantization = False   # setting this 'True' may speed up the process but lower the accuracy
+
+transcriptor = Transcriptor(file, log_folder, language, modelSize, voices_folder, quantization)
+
+res = transcriptor.transcribe()
+
+res --> [["start", "end", "text", "speaker"], ["start", "end", "text", "speaker"]...]
+```
+
+start: starting time of speech in seconds  
+end: ending time of speech in seconds  
+text: transcribed text for speech during start and end  
+speaker: speaker of the text 
+
+voices_folder structure:  
+```
+voices_folder    
+|---> person1      
+|        |---> sample1.wav   
+|        |---> sample2.wav     
+|                ...
+|
+|---> person2  
+|        |---> sample1.wav  
+|        |---> sample2.wav   
+|                ...
+|--> ...  
+```
+
+supported language codes:  
+
+```
+"af", "am", "ar", "as", "az", "ba", "be", "bg", "bn", "bo", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "es", "et", "eu", "fa", "fi", "fo", "fr", "gl", "gu", "ha", "haw", "he", "hi", "hr", "ht", "hu", "hy", "id", "is","it", "ja", "jw", "ka", "kk", "km", "kn", "ko", "la", "lb", "ln", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn","mr", "ms", "mt", "my", "ne", "nl", "nn", "no", "oc", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk","sl", "sn", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "tg", "th", "tk", "tl", "tr", "tt", "uk", "ur", "uz","vi", "yi", "yo", "zh", "yue"
+```
+
+supported language names:
+
+```
+"Afrikaans", "Amharic", "Arabic", "Assamese", "Azerbaijani", "Bashkir", "Belarusian", "Bulgarian", "Bengali","Tibetan", "Breton", "Bosnian", "Catalan", "Czech", "Welsh", "Danish", "German", "Greek", "English", "Spanish","Estonian", "Basque", "Persian", "Finnish", "Faroese", "French", "Galician", "Gujarati", "Hausa", "Hawaiian","Hebrew", "Hindi", "Croatian", "Haitian", "Hungarian", "Armenian", "Indonesian", "Icelandic", "Italian", "Japanese","Javanese", "Georgian", "Kazakh", "Khmer", "Kannada", "Korean", "Latin", "Luxembourgish", "Lingala", "Lao","Lithuanian", "Latvian", "Malagasy", "Maori", "Macedonian", "Malayalam", "Mongolian", "Marathi", "Malay", "Maltese","Burmese", "Nepali", "Dutch", "Norwegian Nynorsk", "Norwegian", "Occitan", "Punjabi", "Polish", "Pashto","Portuguese", "Romanian", "Russian", "Sanskrit", "Sindhi", "Sinhalese", "Slovak", "Slovenian", "Shona", "Somali","Albanian", "Serbian", "Sundanese", "Swedish", "Swahili", "Tamil", "Telugu", "Tajik", "Thai", "Turkmen", "Tagalog","Turkish", "Tatar", "Ukrainian", "Urdu", "Uzbek", "Vietnamese", "Yiddish", "Yoruba", "Chinese", "Cantonese",
+```
+
+### Audio preprocessing example:
+
+```
+from speechlib import PreProcessor
+
+file = "obama1.mp3"
+
+# convert mp3 to wav
+wav_file = PreProcessor.convert_to_wav(file)   
+
+# convert wav file from stereo to mono
+PreProcessor.convert_to_mono(wav_file)
+
+# re-encode wav file to have 16-bit PCM encoding
+PreProcessor.re_encode(wav_file)
+```
+
+### Performance
+```
+These metrics are from Google Colab tests.
+These metrics do not take into account model download times.
+These metrics are done without quantization enabled.
+(quantization will make this even faster)
+
+metrics for faster-whisper "tiny" model:
+    on gpu:
+        audio name: obama_zach.wav
+        duration: 6 min 36 s
+        diarization time: 24s
+        speaker recognition time: 10s
+        transcription time: 64s
+
+
+metrics for faster-whisper "small" model:
+    on gpu:
+        audio name: obama_zach.wav
+        duration: 6 min 36 s
+        diarization time: 24s
+        speaker recognition time: 10s
+        transcription time: 95s
+
+
+metrics for faster-whisper "medium" model:
+    on gpu:
+        audio name: obama_zach.wav
+        duration: 6 min 36 s
+        diarization time: 24s
+        speaker recognition time: 10s
+        transcription time: 193s
+
+
+metrics for faster-whisper "large" model:
+    on gpu:
+        audio name: obama_zach.wav
+        duration: 6 min 36 s
+        diarization time: 24s
+        speaker recognition time: 10s
+        transcription time: 343s
+```
+
+
+This library uses following huggingface models:
+
+#### https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb
+#### https://huggingface.co/Ransaka/whisper-tiny-sinhala-20k-8k-steps-v2
+#### https://huggingface.co/pyannote/speaker-diarization

metrics.txt

@@ -0,0 +1,39 @@
+These metrics are from Google Colab tests.
+These metrics do not take into account model download times.
+These metrics are done without quantization enabled.
+(quantization will make this even faster)
+
+metrics for faster-whisper "tiny" model:
+    on gpu:
+        audio name: obama_zach.wav
+        duration: 6 min 36 s
+        diarization time: 24s
+        speaker recognition time: 10s
+        transcription time: 64s
+
+
+metrics for faster-whisper "small" model:
+    on gpu:
+        audio name: obama_zach.wav
+        duration: 6 min 36 s
+        diarization time: 24s
+        speaker recognition time: 10s
+        transcription time: 95s
+
+
+metrics for faster-whisper "medium" model:
+    on gpu:
+        audio name: obama_zach.wav
+        duration: 6 min 36 s
+        diarization time: 24s
+        speaker recognition time: 10s
+        transcription time: 193s
+
+
+metrics for faster-whisper "large" model:
+    on gpu:
+        audio name: obama_zach.wav
+        duration: 6 min 36 s
+        diarization time: 24s
+        speaker recognition time: 10s
+        transcription time: 343s

pyannote-audio_LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 CNRS
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

requirements.txt

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+transformers
+torch
+torchaudio
+pydub
+pyannote.audio
+speechbrain
+accelerate
+faster-whisper

setup.py

@@ -0,0 +1,24 @@
+from setuptools import find_packages, setup
+
+with open("library.md", "r") as f:
+    long_description = f.read()
+
+setup(
+    name="speechlib",
+    version="1.1.0",  
+    description="speechlib is a library that can do speaker diarization, transcription and speaker recognition on an audio file to create transcripts with actual speaker names. This library also contain audio preprocessor functions.",
+    packages=find_packages(),
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/NavodPeiris/speechlib",
+    author="Navod Peiris",
+    author_email="navodpeiris1234@gmail.com",
+    license="MIT",
+    classifiers=[
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3.10",
+        "Operating System :: OS Independent",
+    ],
+    install_requires=["transformers==4.36.2", "torch==2.1.2", "torchaudio==2.1.2", "pydub==0.25.1", "pyannote.audio==3.1.1", "speechbrain==0.5.16", "accelerate==0.26.1", "faster-whisper==0.10.1", "openai-whisper==20231117"],
+    python_requires=">=3.8",
+)

setup_instruction.md

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+for building setup:
+    pip install setuptools
+    pip install wheel
+
+on root:
+    python setup.py sdist bdist_wheel
+
+for publishing:
+    pip install twine
+
+for install locally for testing:
+    pip install dist/speechlib-1.1.0-py3-none-any.whl
+
+finally run:
+    twine upload dist/*
+
+    fill as follows:
+        username: __token__
+        password: {your token value}